Aerial surveillance is commonly used for investigating the surface of the earth. In particular, images taken by satellites are used to identify objects or materials on the ground.
FIG. 1 illustrates a surveillance system 100 comprising an illumination source 101, such as the sun and a satellite 102 with a camera 104. The camera captures images of the surface of the earth 106. Located on the surface of the earth 106 are in this example a street 108 and plants 110. The street 108 and the plants 110 differ in the way they reflect the light which is the reason why they appear as having different colours to the eye. The measure of reflection of light from different materials is referred to as reflectance. Throughout this specification, the quantitative measure of the reflectance at different wavelengths, that is the spectrum of the reflectance, is referred to as reflectance spectrum. The reflectance spectrum is a material property and therefore invariant to the illumination spectrum. In most cases, the illumination spectrum is not ideal white light and therefore, the received light spectrum is a combination of the illumination spectrum and the reflectance spectrum of the material. The received light spectrum is referred to as radiance.
The camera 104 of satellite 102 is a digital camera with a number of pixels. Each pixel captures a part of the image of the surface of the earth 106. The figure shows an exemplary pixel area 110, which is the area in the scene that is covered by one exemplary pixel. In this example, the pixel area 110 covers only the black street surface. As a result the pixel colour value is black.
Multispectral cameras, deliver not only a colour value or values for three primary colours but many values. These values represent the spectrum of the light reflected from the surface of the earth 106 and are referred to as radiance spectrum. The capture of radiance spectrum is useful since different materials have different characteristic reflectance spectra and therefore, the material can be determined from the captured radiance spectra. In this example, from the radiance spectrum of pixel area 110 it is determined that the material in this area is asphalt concrete.
Due to the limited spatial resolution of camera 104 and the small size of objects on the surface of the earth 106, the pixel area 110 may cover more than one material at one time. As a result, the radiance spectrum at that pixel location is a mixture of the reflectance spectra of the different materials within the pixel area 110. In other words, there are underlying spectra in the radiance spectrum of each pixel that originate from the different materials. The determination of this sub-pixel information, that is the determination of the underlying spectra, such as the material reflectance spectra, is referred to as spectral unmixing.
The illumination spectrum is an additional component to the mixture of the material spectra. It is difficult to find any underlying spectra in the radiance spectra and these underlying spectra are not the material reflectance spectra unless the illumination spectrum is also considered.
FIG. 2 illustrates a transformation 200 of first and second material reflectance spectra 210 and 220 respectively, into a sample space 230. In this simplified example, the illumination spectrum is constant for all wavelengths and the surface of the observed object is an even plane. The first material reflectance spectrum 210 is sampled at two wavelengths λ1 and λ2. This results in reflectance values 211 and 212. The reflectance values 211 and 212 of the first material reflectance are represented by a first endmember 231 in the two-dimensional sample space 230.
Similarly, the second material reflectance spectrum 220 is sampled at the same two wavelengths λ1 and λ2 resulting in reflectance values 221 and 222, which are represented by a second endmember 232 in the sample space 230. In this way, many different material reflectance spectra can be represented in the same sample space. In this example, the sample space comprises a third endmember 233 representing a third material reflectance spectrum (not shown).
It is noted that in most applications the material reflectance spectra are sampled at far more points, such as one hundred. In fact, the sample wavelengths may be the same as the wavelengths of the hyperspectral image data. As a result, the sample space 230 is high-dimensional—one dimension for each wavelength.
The three endmembers 231, 232 and 233 form triangle 235. Under ideal white light the mixture of material reflectance spectra lies within the triangle 235.
In most applications, the material reflectance spectra are unknown and need to be determined. If a sufficiently large number of different mixtures of these three material reflectance spectra is captured, the resulting points fill the triangle 235 and the corners could be identified as the underlying endmembers. However, more than two wavelengths need to be sampled to identify different materials. In the resulting multi-dimensional sample space, the determination of the underlying endmembers is difficult.
One problem with existing approaches is that the underlying spectra, such as endmember spectra, need to be provided to the method as an input. If the underlying spectra are not known, these methods cannot be used.
Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.